Nature of Inquiry and Quantitative Research Notes

Nature of Inquiry and Research

Definition of Inquiry: Inquiry is formally defined as the act of "seeking for truth, information, or knowledge."
Problem-Solving Technique: It serves as a fundamental problem-solving technique used to address unknowns.
Process of Information Gathering: The pursuit of information and data begins with gathering through the application of the different human senses.
Lifespan Application: Individuals carry on the process of inquiry throughout their entire lives, from birth until death.
Synonyms: The term is considered synonymous with the word "investigation."

Understanding Research

Definition of Research: Research is defined as the scientific investigation of phenomena. This process includes the collection, presentation, analysis, and interpretation of facts that align an individual’s speculation with reality.
Structural Requirements: In research, a systematic and well-planned procedure is mandatory to fulfill specific needs. This ensures that information is acquired effectively and evaluated for both accuracy and effectiveness.

Comparative Analysis: Inquiry vs. Research

Inquiry Characteristics

Orientation: Encourages the exploration of questions.
Emphasis: Focuses primarily on the process of discovery.
Scope: Can become broad and expansive very quickly.
Skill Acquisition: Allows students to gain soft skills such as cooperation, self-reflection, and problem-solving.
Complexity: Generally easier to carry out than formal research studies.
Main Aim: Primarily to solve problems, resolve doubts, or augment knowledge.

Research Characteristics

Orientation: Encourages adherence to a formal, established process.
Emphasis: Focuses on efficiency and focus.
Scope: Tends to remain focused and precise rather than expansive.
Skill Acquisition: Allows students to gain technical skills such as organization, communication, and attention to detail.
Complexity: Systematic and formal investigation and study of materials and sources.
Main Aim: Focused on establishing facts, reaching new conclusions, gathering new information, or testing a specific theory.

Fundamentals of Quantitative Research

Core Definition: Quantitative research is an objective, systematic, and empirical investigation of observable phenomena using computational techniques.
Numerical Focus: It highlights the numerical analysis of data with the goal that the numbers yield unbiased results.
Generalization: Findings are intended to be generalized to a larger population to explain a particular observation.
Use of Data: It utilizes scientifically collected and statistically analyzed data to investigate observable phenomena.
Definition of Phenomenon: A phenomenon is any existing or observable fact or situation that a researcher wants to unearth further or understand.

The Research Process Components

Research Question
Variables
Hypotheses
Quantitative Research Design
Sampling
Data Collection
Data Analysis
Results and Conclusions

Characteristics of Quantitative Research

Large Sample Size: To obtain more meaningful statistical results, the data must come from a large sample size.
Objectivity: Data gathering and analysis are performed accurately and objectively. Results are unaffected by the researcher’s intuition or personal guesses.
Concise Visual Presentation: Because data is numerical, it can be presented through graphs, charts, and tables, allowing for better conveyance and interpretation.
Faster Data Analysis: The application of statistical tools allows for a less time-consuming analysis process.
Generalized Data: Data taken from a sample can be applied to the entire population if sampling is done correctly (e.g., sufficient size and random samples).
Fast and Easy Data Collection: The use of standardized research instruments allows researchers to collect data from large samples efficiently.
Reliable Data: Data is taken and analyzed objectively from a representative sample, making it credible for policymaking and decision-making.
High Replicability: The method can be repeated to verify findings, which enhances validity and prevents false or immature conclusions.

Evaluation: Advantages and Disadvantages

Advantages

High level of objectivity.
Numerical and quantifiable data can be used to predict outcomes.
Findings are generalizable to the population.
Establishment of cause and effect is conclusive.
Fast and easy data analysis via statistical software.
Fast and easy data gathering processes.
High replicability for validation.
Strong capacity to establish validity and reliability.

Disadvantages

Lacks the necessary data to explore problems or concepts in extreme depth.
Does not provide comprehensive explanations for human experiences.
Certain information (feelings, beliefs) cannot be described by numerical data.
The research design is rigid and lacks flexibility.
Participants are restricted to choosing only from provided responses.
Respondents may provide inaccurate responses.
Large sample sizes can make data collection costly.

Kinds of Quantitative Research

1. Descriptive Research

Purpose: Seeks to describe the nature, characteristics, and components of a population or phenomenon.
Limitations: No manipulation of variables or search for cause and effect.
Focus: Gathers information about the current status of a phenomenon.
Hypothesis: Does not start with a hypothesis but is likely to develop one.
Example: A study on the level of anxiety felt by Baguio residents during the COVID- $19$ pandemic. A survey is conducted to describe the anxiety, which could later lead to studies on differences across ages.
Other Examples: Attitudes of Grade $12$ students toward research; parents' feelings about opening classes in August.

2. Comparative Research

Purpose: Seeks to identify if there is a significant numerical difference between variables using statistical tools.
Requirement: A hypothesis is established.
Example: Investigating if there is a significant difference in student academic performance between face-to-face learning and online learning.
Other Examples: Attitudes of millennial adults vs. older people regarding online banking; sales values of online vs. non-online sellers.

3. Correlational Research

Purpose: Seeks to establish the degree of relationship among two or more variables without looking into causal reasons.
Example: A study to see if the number of hours spent in learning is related to student assessment scores (e.g., increasing hours from $4$ to $5$ per week).
Other Examples: Relationship between Grade $12$ students' attitudes in research and their grades; relationship between teacher training and digital literacy.

4. Experimental Research

Definition: Also known as true experimentation; applies the scientific method to test cause-and-effect relationships under controlled conditions.
Key Characteristics:
- Control variable (Control Group)
- Manipulated variable (Experimental Group)
- Replication
- Randomization
Example: A teacher randomly assigns students to two groups; one uses a new study technique (Experimental Group) and the other uses usual techniques (Control Group). Test scores are then compared.
Other Examples: The effect of a "math terror" teacher on student attendance; the effect of peer counseling on emotional conditions.

5. Quasi-Experimental Research

Purpose: Attempts to establish cause-and-effect relationships but lacks full control.
Constraint: Participants cannot be randomly assigned to groups. Existing groups or non-random methods (self-selection) are used instead.
Example: Evaluating a new reading program by using two existing classes (one treatment, one traditional) rather than randomly assigning individual students.

6. Survey Research

Purpose: Gathers information from representative samples to describe, compare, or explain trends, attitudes, or behaviors.
Temporal Types:
- Cross-sectional: Data gathered at a single point in time.
- Longitudinal: Data gathered over a long period.
Tools: Questionnaires, online forms, or face-to-face interviews.
Example: NEDA requesting online surveys from MSME owners to draft guidelines after the pandemic.

7. Causal-Comparative (Ex-Post Facto) Research

Definition: "Ex-post facto" means "after the fact." It derives conclusions from observations that already occurred in the past.
Logic: Researchers observe an existing outcome (dependent variable) and work backward to investigate potential causes (independent variable).
Constraints: No manipulation of variables and no random assignment because the "cause" has already occurred.
Medical Example: Studying smoking and lung cancer. Researchers compare medical histories of cancer patients to a healthy control group because it is unethical to ask subjects to smoke.
Psychology Example: Weight status and self-confidence. Grouping teenagers based on pre-existing weight categories to measure confidence.
Difference with Quasi-Experimental: In Quasi-Experimental, the researcher manipulates the independent variable (intervention). In Causal-Comparative, there is no intervention, only comparison of existing conditions.

Importance of Quantitative Research Across Fields

General Value: Crucial for discovering the unknown and finding meaningful solutions to difficulties.
Daily Life Impacts:
- Discovering new facts about known phenomena.
- Developing new instruments or products.
- Satisfying human curiosity.
- Providing a basis for decision-making in business, education, and government.
- Finding answers via scientific methods.
- Promoting health and prolonging life.
- Improving travel, work, and communication (speed and comfort).
- Improving the quality of graduates through upgraded educational practices.

Variables in Research

Definition: Fundamental components representing characteristics, numbers, or quantities that can be measured or quantified. They can take on different values.
Role: Variables are manipulated, measured, or controlled to gain insights into relationships, causes, and effects.

Independent Variable (IV)

Definition: The factor or condition manipulated or varied by the researcher to observe effects.
Nature: Variation does not depend on other variables; it is the cause or stimulus.
Checklist for Identification:
1. Is the variable manipulated or used as a grouping method?
2. Does it come before the other variable in time?
3. Is the researcher trying to see if it affects another variable?

Dependent Variable (DV)

Definition: The outcome or effect that researchers aim to understand.
Nature: Its value depends on the changes in the independent variable.
Checklist for Identification:
1. Is it measured as an outcome?
2. Is it dependent on another variable?
3. Is it measured only after others are altered?

Extraneous Variable

Definition: Other factors that may influence the outcome which are not manipulated or pre-defined by the researcher.

Examples of IV and DV

Tomato Growth: IV = Type of light (fluorescent, incandescent, natural); DV = Rate of growth.
Fasting: IV = Presence of intermittent fasting; DV = Blood sugar levels.
Medical Marijuana: IV = Presence of use; DV = Frequency and intensity of pain.
Remote Work: IV = Environment (remote vs. office); DV = Job satisfaction.

Classifications of Variables

1. Numerical / Quantitative Variables

Variables that are numeric and measurable.

Discrete Variables: Countable whole numbers.
- Examples: Number of siblings ( $0, 1, 2$ ), absences ( $0, 1, 2$ ), books owned ( $5, 10, 20$ ), or children.
- Note: Cannot have fractions (e.g., no $2.5$ absences).
Continuous Variables: Can take any value within a range, including decimals or fractions.
- Height: $160.2\,cm$ , $172.5\,cm$ .
- Weight: $55.5\,kg$ , $63.8\,kg$ .
- Age: $18.0$ , $18.5$ , $19.2$ years.
- Temperature: $36.5^{\circ}C$ , $37.2^{\circ}C$ .
- Income: $P10,000.50$ , $P15,500.75$ .
- Time: $2.5\,hours$ .

2. Categorical / Qualitative Variables

Variables representing groups or characteristics that cannot be meaningfully averaged.

Dichotomous Variables: Consist of only two distinct categories.
- Examples: Smoker status (Smoker, Non-smoker), Passed exam (Yes, No), Vaccinated (Yes, No), Gender (Male, Female in binary classification).
Nominal Variables: Categories with no specific order or ranking.
- Examples: Religion (Catholic, Muslim), Civil status (Single, Married), Favorite color, Brand of cellphone (Apple, Samsung), Type of pet.
Ordinal Variables: Categories with a clear, natural order or rank.
- Examples: Satisfaction level (Very Satisfied to Neutral), Educational attainment (High School, College, PhD), Likert scale ( $1$ to $5$ ), Position in class (Top $1$ , Top $2$ ), Clothing size (Small, Medium, Large).